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  <div class="section" id="numpy-random-generator-standard-normal">
<h1>numpy.random.Generator.standard_normal<a class="headerlink" href="#numpy-random-generator-standard-normal" title="Permalink to this headline">¶</a></h1>
<p>method</p>
<dl class="method">
<dt id="numpy.random.Generator.standard_normal">
<code class="sig-prename descclassname">Generator.</code><code class="sig-name descname">standard_normal</code><span class="sig-paren">(</span><em class="sig-param">size=None</em>, <em class="sig-param">dtype='d'</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span><a class="headerlink" href="#numpy.random.Generator.standard_normal" title="Permalink to this definition">¶</a></dt>
<dd><p>Draw samples from a standard Normal distribution (mean=0, stdev=1).</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl class="simple">
<dt><strong>size</strong><span class="classifier">int or tuple of ints, optional</span></dt><dd><p>Output shape.  If the given shape is, e.g., <code class="docutils literal notranslate"><span class="pre">(m,</span> <span class="pre">n,</span> <span class="pre">k)</span></code>, then
<code class="docutils literal notranslate"><span class="pre">m</span> <span class="pre">*</span> <span class="pre">n</span> <span class="pre">*</span> <span class="pre">k</span></code> samples are drawn.  Default is None, in which case a
single value is returned.</p>
</dd>
<dt><strong>dtype</strong><span class="classifier">{str, dtype}, optional</span></dt><dd><p>Desired dtype of the result, either ‘d’ (or ‘float64’) or ‘f’
(or ‘float32’). All dtypes are determined by their name. The
default value is ‘d’.</p>
</dd>
<dt><strong>out</strong><span class="classifier">ndarray, optional</span></dt><dd><p>Alternative output array in which to place the result. If size is not None,
it must have the same shape as the provided size and must match the type of
the output values.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>out</strong><span class="classifier">float or ndarray</span></dt><dd><p>A floating-point array of shape <code class="docutils literal notranslate"><span class="pre">size</span></code> of drawn samples, or a
single sample if <code class="docutils literal notranslate"><span class="pre">size</span></code> was not specified.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="numpy.random.normal.html#numpy.random.normal" title="numpy.random.normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal</span></code></a></dt><dd><p>Equivalent function with additional <code class="docutils literal notranslate"><span class="pre">loc</span></code> and <code class="docutils literal notranslate"><span class="pre">scale</span></code> arguments for setting the mean and standard deviation.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>For random samples from <img class="math" src="../../../_images/math/2f771078e62b5640eb4d15c51b447940e297fd51.svg" alt="N(\mu, \sigma^2)"/>, use one of:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">mu</span> <span class="o">+</span> <span class="n">sigma</span> <span class="o">*</span> <span class="n">gen</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="o">=...</span><span class="p">)</span>
<span class="n">gen</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mu</span><span class="p">,</span> <span class="n">sigma</span><span class="p">,</span> <span class="n">size</span><span class="o">=...</span><span class="p">)</span>
</pre></div>
</div>
<p class="rubric">Examples</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">default_rng</span><span class="p">()</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">()</span>
<span class="go">2.1923875335537315 #random</span>
</pre></div>
</div>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">s</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="mi">8000</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span>
<span class="go">array([ 0.6888893 ,  0.78096262, -0.89086505, ...,  0.49876311,  # random</span>
<span class="go">       -0.38672696, -0.4685006 ])                                # random</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(8000,)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span> <span class="o">=</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">s</span><span class="o">.</span><span class="n">shape</span>
<span class="go">(3, 4, 2)</span>
</pre></div>
</div>
<p>Two-by-four array of samples from <img class="math" src="../../../_images/math/b9670cf2efaca4d991493dc50edf0bbf7baa678a.svg" alt="N(3, 6.25)"/>:</p>
<div class="doctest highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="mi">3</span> <span class="o">+</span> <span class="mf">2.5</span> <span class="o">*</span> <span class="n">rng</span><span class="o">.</span><span class="n">standard_normal</span><span class="p">(</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="go">array([[-4.49401501,  4.00950034, -1.81814867,  7.29718677],   # random</span>
<span class="go">       [ 0.39924804,  4.68456316,  4.99394529,  4.84057254]])  # random</span>
</pre></div>
</div>
</dd></dl>

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